Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines

Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), pages 817–822, Vancouver, Canada, August 3 - 4, 2017. c⃝2017 Association for Computational Linguistics Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines Youness Mansar⋆, Lorenzo Gatti◦, Sira Ferradans⋆, Marco Guerini◦, and Jacopo Staiano⋆ ⋆Fortia Financial Solutions, Paris, France ◦Fondazione Bruno Kessler, Povo, Italy {youness.mansar,sira.ferradans,jacopo.staiano}@fortia.fr {l.gatti,guerini}@fbk.eu Abstract In this paper, we describe a methodology to infer Bullish or Bearish sentiment to- wards companies/brands. More specifi- cally, our approach leverages affective lex- ica and word embeddings in combination with convolutional neural networks to in- fer the sentiment of financial news head- lines towards a target company. Such ar- chitecture was used and evaluated in the context of the SemEval 2017 challenge (task 5, subtask 2), in which it obtained the best performance. 1 Introduction Real time information is key for decision mak- ing in highly technical domains such as finance. The explosive growth of financial technology in- dustry (Fintech) continued in 2016, partially due to the current interest in the market for Artificial Intelligence-based technologies1. Opinion-rich texts such as micro-blogging and news can have an important impact in the finan- cial sector (e.g. raise or fall in stock value) or in the overall economy (e.g. the Greek public debt crisis). In such a context, having granular access to the opinions of an important part of the popula- tion is of key importance to any public and private actor in the field. In order to take advantage of this raw data, it is thus needed to develop machine learning methods allowing to convert unstructured text into information that can be managed and ex- ploited. 1F. Desai, “The Age of Artificial Intelligence in Fintech” https://www.forbes.com/sites/ falgunidesai/2016/06/30/the-age-of- artificial-intelligence-in-fintech S. Delventhal, “Global Fintech Investment Hits Record High in 2016” http://www.investopedia.com/ articles/markets/061316/global-fintech- investment-hits-record-high-2016.asp In this paper, we address the sentiment analysis problem applied to financial headlines, where the goal is, for a given news headline and target com- pany, to infer its polarity score i.e. how positive (or negative) the sentence is with respect to the tar- get company. Previous research (Goonatilake and Herath, 2007) has highlighted the association be- tween news items and market fluctiations; hence, in the financial domain, sentiment analysis can be used as a proxy for bullish (i.e. positive, upwards trend) or bearish (i.e. negative, downwards trend) attitude towards a specific financial actor, allowing to identify and monitor in real-time the sentiment associated with e.g. stocks or brands. Our contribution leverages pre-trained word embeddings (GloVe, trained on wikipedia+gigaword corpus), the DepecheMood affective lexicon, and convolutional neural networks. 2 Related Works While image and sound come with a natural high dimensional embedding, the issue of which is the best representation is still an open research prob- lem in the context of natural language and text. It is beyond the scope of this paper to do a thorough overview of word representations, for this we refer the interest reader to the excellent review provided by (Mandelbaum and …

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